Transfer learning based multi-fidelity physics informed deep neural network
S Chakraborty - Journal of Computational Physics, 2021 - Elsevier
For many systems in science and engineering, the governing differential equation is either
not known or known in an approximate sense. Analyses and design of such systems are …
not known or known in an approximate sense. Analyses and design of such systems are …
Bi-fidelity variational auto-encoder for uncertainty quantification
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary
objective in model validation. However, achieving this goal entails balancing the need for …
objective in model validation. However, achieving this goal entails balancing the need for …
A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V
The elasto-plastic material behavior, material strength and failure modes of metals
fabricated by additive manufacturing technologies are significantly determined by the …
fabricated by additive manufacturing technologies are significantly determined by the …
A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities
Computing small failure probabilities is often of interest in the reliability analysis of
engineering systems. However, this task can be computationally demanding since many …
engineering systems. However, this task can be computationally demanding since many …
Global sensitivity analysis of a homogenized constrained mixture model of arterial growth and remodeling
Growth and remodeling in arterial tissue have attracted considerable attention over the last
decade. Mathematical models have been proposed, and computational studies with these …
decade. Mathematical models have been proposed, and computational studies with these …
Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
Precipitation forecasting is an important mission in weather science. In recent years, data-
driven precipitation forecasting techniques could complement numerical prediction, such as …
driven precipitation forecasting techniques could complement numerical prediction, such as …
Analysis of the Validity of P2D Models for Solid-State Batteries in a Large Parameter Range
S Sinzig, CP Schmidt, WA Wall - Journal of The Electrochemical …, 2024 - iopscience.iop.org
Simulation models are nowadays indispensable to efficiently assess or optimize novel
battery cell concepts during the development process. Electro-chemo-mechano models are …
battery cell concepts during the development process. Electro-chemo-mechano models are …
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis
The efficient representation of random fields on geometrically complex domains is crucial for
Bayesian modelling in engineering and machine learning, including Gaussian process …
Bayesian modelling in engineering and machine learning, including Gaussian process …
Comparison of optimization parametrizations for regional lung compliance estimation using personalized pulmonary poromechanical modeling
C Laville, C Fetita, T Gille, PY Brillet, H Nunes… - … and Modeling in …, 2023 - Springer
Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF) or post-COVID-19
pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible …
pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible …
Adaptive Gaussian process regression for efficient building of surrogate models in inverse problems
P Semler, M Weiser - Inverse Problems, 2023 - iopscience.iop.org
In a task where many similar inverse problems must be solved, evaluating costly simulations
is impractical. Therefore, replacing the model y with a surrogate model ys that can be …
is impractical. Therefore, replacing the model y with a surrogate model ys that can be …